National Changhua University of Education Institutional Repository : Item 987654321/15982
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 6491/11663
造访人次 : 24630571      在线人数 : 90
RC Version 3.2 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 进阶搜寻


题名: A Hybrid Unscented Kalman Filter and Support Vector Machine Model in Option Price Forecasting
作者: Huang, Shian-Chang;Wu, Tung-Kuang
贡献者: 資訊管理學系
日期: 2006
上传时间: 2013-04-22T07:37:32Z
出版者: Springer Berlin/Heidelberg
摘要: This study develops a hybrid model that combines unscented Kalman filters (UKFs) and support vector machines (SVMs) to implement an online option price predictor. In the hybrid model, the UKF is used to infer latent variables and make a prediction based on the Black- Scholes formula, while the SVM is employed to capture the nonlinear residuals between the actual option prices and the UKF predictions. Taking option data traded in Taiwan Futures Exchange, this study examined the forecasting accuracy of the proposed model, and found that the new hybrid model is superior to pure SVM models or hybrid neural network models in terms of three types of options. This model can also help investors for reducing their risk in online trading.
關聯: Advances in Natural Computation, Lecture Notes in Computer Science, 4221: 303-312
显示于类别:[資訊管理學系所] 期刊論文


档案 大小格式浏览次数
2060300410004.pdf6KbAdobe PDF447检视/开启



DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈